A Review of Different Data Mining Techniques Used in Big Data Applications

Abstract

Big data includes a substantial amount of unorganized, unclear, and inaccurate data. Big data can’t be treated by using traditional data handling tools. Big data is produced from many fields, including analytics, health care, social networking, and so on. Big Data analytics is the capacity to generate beneficial data from such enormous datasets. The capacity to extract precious information from these large data sets is the mining method of large data. In the field of Big Data Analytics, data mining (DM) techniques provide great assistance. DM techniques such as Classification techniques (Naïve Bayes, Neural network, Decision tree, Genetic algorithm, Clustering algorithms (K-nearest neighbors (KNN), support vector machine (SVM)), Regression, Association Rules Mining, Rough Sets Analysis are used to process massive data. In this chapter of the book, we evaluate how distinct DM methods have developed to deal with big data analysis methods. A comprehensive study is presented on various processes of big DM techniques such as dimensionality reduction, clustering, and classification for big data analysis. This book chapter analyzes DM algorithms implemented with Hadoop and Spark technology. Using the MapReduce framework, we also addressed hybrid DM algorithms. We discussed neutrosophic association rule mining for mining big data efficiently and effectively. We also focus on how various techniques of DM are used in the fields of health care and agriculture to fix big data problems.